Rapid Diagnostic Test Versus Microscopy for Diagnosing Malaria Among Pregnant Women in a Resource-Poor Setting; A Cross-Sectional Comparative Study
Bibliographic record
Abstract
BACKGROUND: Diagnostic challenge of malaria in Nigeria remarkably impedes the World Health Organization (WHO) recommendation of laboratory diagnosis before treatment. Rapid Diagnostic Test (RDT) is easier and cheaper to perform when compared with microscopy especially in resource-poor settings. However there are conflicting results on the accuracy of RDT versus microscopy from previous studies. AIM: To compare the overall accuracy of microscopy and RDT in detecting peripheral malaria among pregnant women with clinical features of malaria. MATERIALS & METHODS: This was a cross-sectional comparative studyin whichRDT, microscopy and polymerase chain reaction (PCR) were performed using the peripheral bloodof the eligible study participants at the Alex Ekwueme Federal University Teaching Hospital, Abakaliki between September 1, 2016 and March 31, 2017.The PCR was used as the gold standard in this study. Data was analyzed with the Statistical Package for Social Sciences version 18 (IBM SPSS, Chicago, USA). P value ≤ 0.05 was considered statistically significant. RESULTS: The actual prevalent rates of malaria based on RDT, microscopy and PCR results among the participants were 58.2%, 59.9% and 61.1% respectively. There was no statistical significant difference among RDT, microscopy and combined RDT and microscopy on overall accuracy. Malaria infestation was associated with self-employed and unemployed women, primigravidity, second trimester, rural residence, non-use of long lasting insecticide treated nets and intermittent preventive therapy for malaria. CONCLUSION: There was no difference in overall accuracy among RDT, microscopy and combined RDT and microscopy. This underscores the need to scale up RDT for every patient with clinical features of malaria before treatment in this environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".